Virtual screening and experimental validation identify novel modulators of nuclear receptor RXRα from Drugbank database.

Retinoid X receptor alpha (RXRα), an important ligand-dependent transcription factor, plays a critical role in the development of various cancers and metabolic and neurodegenerative diseases. Therefore, RXRα represents one of the most important targets in modern drug discovery. In this study, Drugbank 2.0 with 1280 old drugs were virtually screened by Glide according to the crystal structure of ligand-binding domain (LBP) of RXRα. 15 compounds selected were tested for their binding and transcriptional activity toward RXRα by Biacore and reporter gene assay, respectively. The identified new scafford ligand of RXRα, Pitavastatin (1), was chemically optimized. Our results demonstrated that statin compounds Pitavastatin (1) and Fluvastatin (4) could bind to the LBP of RXRα (KD=13.30µM and 11.04µM, respectively) and serve as transcriptional antagonists of RXRα. On the contrary, compound (12) (domperidone) and (13) (rosiglitazone maleate) could bind to the LBP of RXRα (KD=8.80µM and 15.01µM, respectively) but serve as transcriptional agonists of RXRα.

Binding characterization, synthesis and biological evaluation of RXRα antagonists targeting the coactivator binding site.

Previously we identified the first retinoid X receptor-alpha (RXRα) modulators that regulate the RXRα biological function via binding to the coregulator-binding site. Here we report the characterization of the interactions between the hit molecule and RXRα through computational modeling, mutagenesis, SAR and biological evaluation. In addition, we reported studies of additional new compounds and identified a molecule that mediated the NF-κB pathway by inhibiting the TNFα-induced IκBα degradation and p65 nuclear translocation.

Prediction Distribution Model of Moisture Content in Laminated Wood Components.

Shrinkage cracks are some of the most common defects in timber structures obtained from woods with an uneven distribution of moisture content and are subject to external dynamic environmental changes. To accurately predict the changes in the moisture content of wood components at any time and position, this study first applied the principles of food drying and established a moisture field model for laminated wood based on the analogy between heat and humidity transfer. A model for predicting the moisture content of wood that considers time and spatial distribution was then proposed. Second, by collecting relevant experimental data and establishing a finite element analysis model, three moisture absorption conditions (0-9.95%, 0-13.65%, and 0-17.91%) and four desorption conditions (34-5.5%, 28-8.3%, 31-11.8%, and 25.5-15.9%) were analyzed. In the moisture absorption comparison, the time needed to reach 95% equilibrium moisture content was 2.43 days, 4.07 days, and 6.32 days. The rate at which the internal components reached equilibrium moisture content exceeded 10 days. The temporal and spatial distribution of wood moisture content revealed the correctness of the proposed wood moisture field model. Finally, the moisture content prediction model was applied in the order of characteristic equation solutions, moisture content gradient difference, and laminated wood size. The results revealed that the established humidity field model can predict the wood moisture content and how it changes over time and in space. Notably, 1-2 orders for the solution of the characteristic equation are recommended when applying the prediction model. The greater the difference in moisture content, the faster the equilibrium moisture content is reached. The moisture content varies greatly based on the component size and position. Notably, the influence of moisture gradient and wood size on the average wood moisture content cannot be ignored.

Machine-learning enhanced dark soliton detection in Bose-Einstein condensates.

Most data in cold-atom experiments comes from images, the analysis of which is limited by our preconceptions of the patterns that could be present in the data. We focus on the well-defined case of detecting dark solitons-appearing as local density depletions in a Bose-Einstein condensate (BEC)-using a methodology that is extensible to the general task of pattern recognition in images of cold atoms. Studying soliton dynamics over a wide range of parameters requires the analysis of large datasets, making the existing human-inspection-based methodology a significant bottleneck. Here we describe an automated classification and positioning system for identifying localized excitations in atomic BECs utilizing deep convolutional neural networks to eliminate the need for human image examination. Furthermore, we openly publish our labeled dataset of dark solitons, the first of its kind, for further machine learning research.

Feedback induced magnetic phases in binary Bose-Einstein condensates.

Weak measurement in tandem with real-time feedback control is a new route toward engineering novel nonequilibrium quantum matter. Here we develop a theoretical toolbox for quantum feedback control of multicomponent Bose-Einstein condensates (BECs) using backaction-limited weak measurements in conjunction with spatially resolved feedback. Feedback in the form of a single-particle potential can introduce effective interactions that enter into the stochastic equation governing system dynamics. The effective interactions are tunable and can be made analogous to Feshbach resonances-spin independent and spin dependent-but without changing atomic scattering parameters. Feedback cooling prevents runaway heating due to measurement backaction and we present an analytical model to explain its effectiveness. We showcase our toolbox by studying a two-component BEC using a stochastic mean-field theory, where feedback induces a phase transition between easy-axis ferromagnet and spin-disordered paramagnet phases. We present the steady-state phase diagram as a function of intrinsic and effective spin-dependent interaction strengths. Our result demonstrates that closed-loop quantum control of Bose-Einstein condensates is a powerful tool for quantum engineering in cold-atom systems.

BI1071, a Novel Nur77 Modulator, Induces Apoptosis of Cancer Cells by Activating the Nur77-Bcl-2 Apoptotic Pathway.

Nur77 (also called TR3 or NGFI-B), an orphan member of the nuclear receptor superfamily, induces apoptosis by translocating to mitochondria where it interacts with Bcl-2 to convert Bcl-2 from an antiapoptotic to a pro-apoptotic molecule. Nur77 posttranslational modification such as phosphorylation has been shown to induce Nur77 translocation from the nucleus to mitochondria. However, small molecules that can bind directly to Nur77 to trigger its mitochondrial localization and Bcl-2 interaction remain to be explored. Here, we report our identification and characterization of DIM-C-pPhCF3 +MeSO3 - (BI1071), an oxidized product derived from indole-3-carbinol metabolite, as a modulator of the Nur77-Bcl-2 apoptotic pathway. BI1071 binds Nur77 with high affinity, promotes Nur77 mitochondrial targeting and interaction with Bcl-2, and effectively induces apoptosis of cancer cells in a Nur77- and Bcl-2-dependent manner. Studies with animal model showed that BI1071 potently inhibited the growth of tumor cells in animals through its induction of apoptosis. Our results identify BI1071 as a novel Nur77-binding modulator of the Nur77-Bcl-2 apoptotic pathway, which may serve as a promising lead for treating cancers with overexpression of Bcl-2.

Nitrostyrene Derivatives Act as RXRα Ligands to Inhibit TNFα Activation of NF-κB.

Retinoid X receptor alpha (RXRα) and its N-terminally truncated version, tRXRα, are widely implicated in cancer development and represent intriguing targets for cancer prevention and treatment. Successful manipulation of RXRα and tRXRα requires the identification of their modulators that could produce therapeutic effects. Here, we report that a class of nitrostyrene derivatives bind to RXRα by a unique mechanism, of which the nitro group of nitrostyrene derivatives and Cys432 of RXRα are required for binding. The binding results in the potent activation of Gal4-DBD-RXRα-LBD transactivation. However, the binding inhibits the transactivation of RXRα homodimer, which might be due to the distinct conformation of RXRα homodimer induced by these nitrostyrene derivatives. Two RXRα point mutants with Cys432 substituted with Tyr and Trp, respectively, could mimic the bindings of two nitrostyrene derivatives and have the ability of autotransactivation. In studying the functional consequences of the binding, we show that these nitrostyrene derivatives could potently inhibit the TNFα/NFκB signaling pathway in a tRXRα-dependent manner. tRXRα promotes TNFα-induced NF-κB activation through its interaction with TRAF2 and enhances TNFα-induced ubiquitination of RIP1, which is strongly inhibited by nitrostyrene derivatives. The inhibition of TNFα-induced NF-κB activation results in the synergistic effect of the combination of nitrostyrene derivatives and TNFα on the induction of cancer cell apoptosis. Together, our results show a new class of RXRα modulators that induce apoptosis of cancer cells through their unique binding mode and new mechanism of action.